Other visualization techniques

library(ggplot2)
library(dplyr)
library(plotly)
library(igraph)
library(ggdendro)
library(ggmap)
library(gganimate)

Overview

We will cover

• visualizing data with geographical information
• visualizing network data (similar to python’s networkx)
• visualizing tree-like structures
• dynamic visualization

ggmap

#get the stamen map of pullman, wa
map <- get_stamenmap(bbox = c(-117.1670, 46.7350, -117.12, 46.75), zoom = 15, maptype = "toner-lite")

ℹ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

ggmap(map)

igraph

The dataset

• the dataset contains relationships among several media outlets, if one is referenced by mention or hyperlink then they are “connected”

nodes <- read.csv(file ='data/Dataset1-Media-Example-NODES.csv', header = T, as.is = T)

edges <- read.csv("data/Dataset1-Media-Example-EDGES.csv", header = T, as.is = T)

head(nodes)

   id               media media.type type.label audience.size
1 s01            NY Times          1  Newspaper            20
2 s02     Washington Post          1  Newspaper            25
3 s03 Wall Street Journal          1  Newspaper            30
4 s04           USA Today          1  Newspaper            32
5 s05            LA Times          1  Newspaper            20
6 s06       New York Post          1  Newspaper            50

Create igraph object

• directed = T creates a digraph
• note d = links, vertices = nodes and “media” in net

net <- graph_from_data_frame(d = edges, vertices = nodes, directed = T)
net

IGRAPH e677085 DNW- 17 49 -- 
+ attr: name (v/c), media (v/c), media.type (v/n), type.label (v/c),
| audience.size (v/n), type (e/c), weight (e/n)
+ edges from e677085 (vertex names):
 [1] s01->s02 s01->s03 s01->s04 s01->s15 s02->s01 s02->s03 s02->s09 s02->s10
 [9] s03->s01 s03->s04 s03->s05 s03->s08 s03->s10 s03->s11 s03->s12 s04->s03
[17] s04->s06 s04->s11 s04->s12 s04->s17 s05->s01 s05->s02 s05->s09 s05->s15
[25] s06->s06 s06->s16 s06->s17 s07->s03 s07->s08 s07->s10 s07->s14 s08->s03
[33] s08->s07 s08->s09 s09->s10 s10->s03 s12->s06 s12->s13 s12->s14 s13->s12
[41] s13->s17 s14->s11 s14->s13 s15->s01 s15->s04 s15->s06 s16->s06 s16->s17
[49] s17->s04

Create graph

Two sub-networks; wall street journal (has the most edges)

Code

#remove multiple edges and loops
net1 <- simplify(net, remove.multiple = T, remove.loops = T)
plot(net1, edge.arrow.size = 0.4, edge.color = "grey", vertex.label = V(net)$media, vertex.label.color = "black")

ggdendro

head(USArrests)

           Murder Assault UrbanPop Rape
Alabama      13.2     236       58 21.2
Alaska       10.0     263       48 44.5
Arizona       8.1     294       80 31.0
Arkansas      8.8     190       50 19.5
California    9.0     276       91 40.6
Colorado      7.9     204       78 38.7

Code

hc <- hclust(dist(USArrests), "ave") #clustering
ggdendrogram(hc, rotate = T) #rotate the dendrogram by 90 degrees

Dynamic visualizations

• Interactive visualizations allow a user to interact with a plot
• usually presents relationships between specific features of data as other features change
• plotly creates interactive plots and gganimate creates animated plots

plotly

Important

In order to take a screenshot of a plotly plot, we use the library webshot and phantomjs

install.packages("webshot")
webshot::install_phantomjs()

Plotting

Example 1

Code

p <- plot_ly(iris, x = ~Petal.Length, y = ~Petal.Width, color = ~Species, type = "scatter", mode = "markers") %>%
    layout(xaxis = list(title = "Petal Length"), yaxis = list(title = "Petal Width"))

library(shiny)
div(p)

Example 2

Code

p1 <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box") %>%
    layout(xaxis = list(title = "percent of educated population"))

p1

Screenshot

library(htmlwidgets)
htmlwidgets::saveWidget(p1, "p1.html", selfcontained = F)

gganimate

Code

library(gapminder)
library(gganimate)
library(gifski)


ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')